U.S. patent number 9,488,969 [Application Number 14/032,847] was granted by the patent office on 2016-11-08 for configuring building energy management systems using knowledge encoded in building management system points lists.
This patent grant is currently assigned to International Business Machines Corporation. The grantee listed for this patent is International Business Machines Corporation. Invention is credited to Niall Brady, Freddy Lecue, Anika Schumann, Olivier Verscheure.
United States Patent |
9,488,969 |
Brady , et al. |
November 8, 2016 |
Configuring building energy management systems using knowledge
encoded in building management system points lists
Abstract
Techniques for configuring a Building Energy Management System
(BEMS) using knowledge encoded in BMS point lists are provided. In
one aspect, a method for configuring a BEMS of a site is provided.
The method includes the following steps. A knowledge base is
derived from subject matter expertise. The knowledge base is used
to extract one or more building characteristics from a point list
of a building management system (BMS) that are not directly
available from the BMS point list. The BEMS is configured using the
one or more building characteristics extracted from the BMS point
list.
Inventors: |
Brady; Niall (Donadea,
IE), Lecue; Freddy (Castleknock, IE),
Schumann; Anika (Mulhuddart, IE), Verscheure;
Olivier (Dunboyne, IE) |
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
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Assignee: |
International Business Machines
Corporation (Armonk, NY)
|
Family
ID: |
50881824 |
Appl.
No.: |
14/032,847 |
Filed: |
September 20, 2013 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20140163753 A1 |
Jun 12, 2014 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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13711012 |
Dec 11, 2012 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05B
15/02 (20130101); G06Q 50/06 (20130101); G05B
2219/2642 (20130101) |
Current International
Class: |
G06F
1/26 (20060101); G05B 15/02 (20060101); G06Q
50/06 (20120101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Q Zhou et al., "A Model-Based Fault Detection and Diagnosis
Strategy for HVAC Systems," International Journal of Energy
Research, 33(10):903-918 (2009). cited by applicant.
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Primary Examiner: Shechtman; Sean
Attorney, Agent or Firm: Goudy; Kurt P. Chang, LLC; Michael
J.
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATION(S)
This application is a continuation of U.S. application Ser. No.
13/711,012 filed on Dec. 11, 2012, the disclosure of which is
incorporated by reference herein.
Claims
What is claimed is:
1. An apparatus for configuring a building energy management system
(BEMS) of a site, the apparatus comprising: a memory; and at least
one processor device, coupled to the memory, operative to: obtain
information from a user about energy assets in the site, wherein
the information identifies control points for energy asset types;
derive a knowledge base from the information by using the control
points identified for the energy asset types to prepare control
point signature templates for each of the energy asset types; use
the knowledge base to extract one or more building characteristics
from a point list of a building management system (BMS) that are
not directly available from the BMS point list by searching the BMS
point list using the control point signature templates for each of
the energy asset types to establish a presence of the energy assets
within the site, wherein the presence of the energy assets within
the site is not directly available from the BMS point list;
automatically configure the BEMS using the one or more building
characteristics extracted from the BMS point list; and use the BEMS
as a computer-implemented control structure to manage the energy
assets at the site.
2. The apparatus of claim 1, wherein the one or more building
characteristics extracted from the point list comprises data
relating to a physical and a logical network of the site.
3. The apparatus of claim 2, wherein the at least one processor
device is further operative to: identify a model of the BMS using
terms that are unique to the BMS type.
4. The apparatus of claim 1, wherein the one or more building
characteristics extracted from the point list comprises data
related to kinds and numbers of energy assets on the site.
5. The apparatus of claim 4, wherein the at least one processor
device is further operative to: determine a logical location of the
energy assets in a logical network of the site from the knowledge
base.
6. The apparatus of claim 4, wherein the at least one processor
device is further operative to: retrieve data labels for each of
the energy assets on the site from the point list.
7. The apparatus of claim 1, wherein the one or more building
characteristics extracted from the point list comprises data
related to what specific types of energy assets are on the
site.
8. An article of manufacture for configuring a BEMS of a site,
comprising a non-transitory machine-readable recordable medium
containing one or more programs which when executed implement the
steps of: obtaining information from a user about energy assets in
the site, wherein the information identifies control points for
energy asset types; deriving a knowledge base from the information
by using the control points identified for the energy asset types
to prepare control point signature templates for each of the energy
asset types; using the knowledge base to extract one or more
building characteristics from a point list of a building management
system (BMS) that are not directly available from the BMS point
list by searching the BMS point list using the control point
signature templates for each of the energy asset types to establish
a presence of the energy assets within the site, wherein the
presence of the energy assets within the site is not directly
available from the BMS point list; automatically configuring the
BEMS using the one or more building characteristics extracted from
the BMS point list; and using the BEMS as a computer-implemented
control structure to manage the energy assets at the site.
Description
FIELD OF THE INVENTION
The present invention relates to automatic (or semi-automatic)
configuration of a Building Energy Management System (BEMS) and
more particularly, to techniques for configuring a BEMS using
knowledge encoded in BMS point lists.
BACKGROUND OF THE INVENTION
The detection of energy waste that can result from operational
faults (for example if a room is heated and cooled simultaneously)
or from faults of heating, ventilation, or air-conditioning (HVAC)
equipment can translate into a significant energy savings
especially if these faults are detected right away. For example, an
estimated 15% to 30% of energy could be saved if faults in the HVAC
system and its operation could be detected in a timely manner. See,
for example, Q. Zhou et al., "A Model-Based Fault Detection and
Diagnosis Strategy for HVAC Systems," International Journal of
Energy Research, 33(10):903-918 (2009).
Currently, the configuration of a Building Energy Management System
(BEMS) is a manual process. Efforts have been made to automate the
process. See, for example, U.S. Patent Application Publication
Number 2011/0055748, filed by Vacariuc, entitled "Systems and
Methods for Mapping Building Management System Inputs" (hereinafter
"U.S. Patent Application Publication Number 2011/0055748"). U.S.
Patent Application Publication Number 2011/0055748 provides a
process for semi-automatically linking data points of a Building
Management System (BMS) to energy assets (such as air handling
units, boilers, and chillers).
There are however some notable drawbacks to the method of U.S.
Patent Application Publication Number 2011/0055748. Namely, the
linkage between BMS data points and energy assets is not sufficient
for configuring BEMS automatically, because the process requires
local knowledge of what energy assets are available in the
building. The automatic component of the process consists simply of
a string matching approach which is of limited value, and the
process is not fully automatic and does require user input.
Thus, fully automated techniques for configuring a BEMS that
overcomes the above-described issues associated with known
solutions would be desirable.
SUMMARY OF THE INVENTION
The present invention relates to techniques for configuring a
Building Energy Management System (BEMS) using knowledge encoded in
BMS point lists. In one aspect of the invention, a method for
configuring a BEMS of a site is provided. The method includes the
following steps. A knowledge base is derived from subject matter
expertise. The knowledge base is used to extract one or more
building characteristics from a point list of a building management
system (BMS) that are not directly available from the BMS point
list. The BEMS is configured using the one or more building
characteristics extracted from the BMS point list.
A more complete understanding of the present invention, as well as
further features and advantages of the present invention, will be
obtained by reference to the following detailed description and
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a schematic diagram illustrating an exemplary methodology
for capturing generic domain knowledge in an entity relationship
knowledge base and exploiting this entity relationship knowledge
base for automatically configuring a Building Energy Management
System (BEMS) based on a Building Management System (BMS) point
list according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating an exemplary BMS point list
according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating the steps of the methodology of
FIG. 1, with the knowledge base needed in an exemplary scenario to
configure the BEMS rule "Trigger alert if heating and cooling occur
simultaneously" based on the BMS point list (PL) for a site
according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the step of modeling the
BMS in the exemplary scenario of FIG. 3 according to an embodiment
of the present invention;
FIG. 5 is a schematic diagram illustrating the step of determining
the logical location of the energy assets in the BMS network in the
exemplary scenario of FIG. 3 according to an embodiment of the
present invention;
FIG. 6 is a schematic diagram illustrating the step of extracting
the data labels of the energy assets in the BMS network in the
exemplary scenario of FIG. 3 according to an embodiment of the
present invention;
FIG. 7 is a schematic diagram illustrating the step of determining
the types of energy assets from the extracted data labels in the
exemplary scenario of FIG. 3 according to an embodiment of the
present invention;
FIG. 8 is a schematic diagram illustrating the step of configuring
BEMS rules for the asset types in the exemplary scenario of FIG. 3
according to an embodiment of the present invention; and
FIG. 9 is a diagram illustrating an exemplary apparatus for
performing one or more of the methodologies presented herein
according to an embodiment of the present invention.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
Provided herein are techniques for configuring a Building Energy
Management System (BEMS) fully automatically (or
semi-automatically) based on a Building Management System (BMS)
point list (PL). It is notable that even if the BMS point list does
not allow the BEMS configuration fully automatically the present
techniques may be used to configure the BEMS semi-automatically,
i.e., reducing the amount of required manual effort significantly,
e.g., to 5%.
A point list (PL) contains a list of control points related to a
control operation (e.g., sensing, controlling, etc.). The point
list basically specifies the input and output points for a task.
With large BMSs, the PL can be quite large. For instance, in a BMS
of a large building, multiple controllers may be employed to
control multiple energy assets--see, for example, U.S. Patent
Application Publication Number 2011/0055748--and a PL might be
associated with each of the controllers. Thus the PLs can end up
being quite extensive. No conventional approaches exist that fully
automatically configure (parts) of the BEMS.
In general, the present techniques involve capturing generic domain
knowledge in an entity relationship knowledge base and exploiting
this entity relationship knowledge base for automatically
configuring a BEMS based on a Building Management System (BMS)
point list (PL) for a particular site/location. As will be
described in detail below, by way of example only, the present
techniques are performed to configure one or more rules for the
BEMS. One exemplary embodiment for achieving this goal is
illustrated generally in FIG. 1.
The starting point of the process is a point list (PL). Thus in
step 100 one or more point lists (PLs) for the site are obtained.
An example of a Metasys.RTM. BMS point list is shown in FIG. 2.
Some of the fields shown (such as the Status field), are unique to
a Metasys.RTM. BMS point list and are being shown merely as an
example. As shown in FIG. 2, the PL entries in this example each
contain a Status field (e.g., Normal, Alarm, etc.), a Name/Label
field (e.g., Mixed Air Temperature (MA-T), Return Air Temperature
(MA-T), etc.), a Value and a Units/States field (e.g., 71 degrees
Fahrenheit (.degree. F.), a Description field (e.g., building 001,
Air Handling Unit (AHU) 14, etc.), a Location field (e.g.,
DX-87>MA-T), and a Type field (e.g., A1--analog input). It is to
be understood that the point list shown in FIG. 2 is only an
example, and that the present techniques can operate with other
types/configurations of BMS point lists.
In step 102, a knowledge base is derived from subject matter
expertise. By way of example only, a user (such as a building
manager) might be queried as to the relationship of certain energy
assets in the site (i.e., building, energy asset class, or type
within an asset class). The term "asset class" refers for example
to boilers, air handling units (AHUs), etc. The term "type within
asset class" refers for example to Boiler Type--gas fired, oil
fired, AHU type--single deck, dual deck, etc.
The following are examples of how the knowledge base is derived
from subject matter expertise:
Example 1: We use the fact that all BMS's follow a distributed
network design, i.e., all devices are controlled locally via a
series of distributed input/output controllers located close to the
energy asset. This allows us to exploit this clustering effect to
uniquely identify energy assets within specific locations within a
site.
Example 2: We use the fact that certain energy assets will always
have specific control points present to control the
asset--therefore we can prepare a series of signature templates for
each energy asset type that we can search for within the PL to
establish the presence of the asset type within the site.
The knowledge base may be expressed as a series of rules that will
be employed to extract information (i.e., building characteristics)
from a BMS PL, see below. It is notable that the information
extracted from the BMS PL using the present techniques is
information that is not directly available from the BMS PL. The
term "directly available" means information that is listed
explicitly in any of the columns of the PL like the unit value.
See, for example, the PL in FIG. 2 where the unit values are given
explicitly in the PL. By comparison, the present techniques can be
used to extract information that is not specifically listed in the
BMS PL. By way of example only, building characteristics might
include, but are not limited to, building energy characteristics
energy asset types and sub-types, etc.
Starting from the point list of the BMS and the knowledge base one
or more building characteristics are extracted. To begin the data
extraction process, in step 104, the model of the BMS network is
identified by extracting from the point list terms that are unique
to a given BMS type. The term "model," as used herein simply refers
to the type of BMS (e.g., Metasys.RTM., TransIQ.RTM., etc). If, for
example, a data dump from the Metasys.RTM. BMS is generated, then
the BMS model of that point list is Metasys.RTM..
In step 106, a list of the energy assets in the building is created
and the (logical address) location of each of the energy assets in
the logical BMS network is determined. For each BMS type (see
description of step 110, below) information of how to extract the
logical location of energy assets is provided in the knowledge
base. It is independent of the type and number of energy
assets.
Given the energy assets and their logical locations in the network,
next in step 108 data labels are retrieved for each of the energy
assets. This information can be extracted from the point list
(PL).
In step 110, a determination is made as to the specific types
(kinds) of the energy assets in the building and their number,
e.g., how many air handling units of which type exist in the site.
By performing the above-described steps to identify the specific
types of the energy assets at the site the BMS data points can be
linked to the specific inputs needed by the BEMS. This permits
configuration of BEMS rules for the site in step 112. Again this
step involves using the knowledge base to extract building
characteristics (in this case data related to the specific
kind/types and number of the energy assets on the site)--these
characteristics which are not directly available from the point
list (PL).
Each of the above-described steps is now further described in
detail by way of reference to an exemplary, non-limiting example
involving the configuration of rules for a BEMS. While the
following example relates to the configuration of rules for a BEMS,
the same principles and steps would generally apply to the
configuration of any other relevant building control system (e.g.,
fire safety control system, building access system, etc.).
In the following example, the present techniques are employed to
automatically configure a BEMS rule "Trigger alert if heating and
cooling occur simultaneously" based on a BMS point list. The
extract of the knowledge base that is needed to configure this rule
automatically based on the BMS point list (PL) is given as
follows:
1. IF PL contains string "metasys" THEN BMSmodel:=metasys
2. IF BMSmodel=metasys THEN a unique location LOC is characterized
by the unique pair of ("NAE", "DX")
3. IF for a location LOC the set of labels contains "CLG-VLV" and
"HTG-VLV" and none of the labels "HD-T", "CD-T", "MA-T", and
"COIL-T" THEN assetType(NAE, DX):=SDVATF
4. IF assetType(LOC)=SDVATF THEN configure
ruleSimultaneousHeatingAndCooling((LOC, CLG-VLV), (LOC,
HTG-VLV))
See, for example, FIG. 3 which illustrates the methodology of FIG.
1 (described above), with the knowledge base needed in this
particular example to configure the BEMS rule "Trigger alert if
heating and cooling occur simultaneously" based on the BMS point
list (PL). As shown in FIG. 3, based on the given point list (PL)
and knowledge base (rules 1-4), the steps 104-112 can be performed
to configure rules for the BEMS. Each of the steps will now be
described in detail in the context of this present example.
First, using rule 1 from the knowledge base, string matching is
used to identify the type of BMS of the PL. This determines how
searching is done for all data points of a unique energy asset. See
FIG. 4 which illustrates schematically step 104 being performed in
this exemplary scenario to model the BMS. As provided above, the
knowledge base rule 1 states that IF PL contains string "metasys"
THEN BMSmodel:=metasys. As shown in FIG. 4, two point lists are
provided, one labeled "Metasys (Rochester)" and the other "TrendlQ
(Dublin)." The points list references are generated from
Metasys.RTM. and TrendIQ.RTM. two of many commercially available
Building Management Systems that are used in industry to manage
building operations primarily in the area of air conditioning
provisioning. In this particular example, rule 1 dictates selection
of the Metasys BMS point list (PL). For instance, each building is
managed by a single BMS. BMSs can have different types, like
Metasys.RTM. or TrendIQ.RTM.. Thus, given a PL one first needs to
identify which BMS type is used. As provided above, knowing the
type of BMS of the point list (here Metasys) is needed in order to
determine how data points of the energy assets in the suite are
searched. It is notable that further iterations of the method may
be conducted with the Trend1Q BMS if so desired, and the use of the
Metasys BMS in this example is merely arbitrary.
Next, in the case of the Metasys.RTM. BMS all data points (from the
PL) are associated to a unique set of identifiers which denote
their unique location in the logical BMS network (rule 2). In the
present Metasys.RTM. example, a pair of unique Network Automation
Engine (NAE) and DX identifiers denotes the unique location of the
energy assets in the logical BMS network. However, any other
suitable identifiers may be employed in the same manner so long as
they uniquely identify the (logical address) location of each of
the energy assets in the logical network.
See FIG. 5 which illustrates schematically step 106 being performed
in this exemplary scenario to determine the logical location of the
energy assets in the BMS network. As shown in FIG. 5, in this
example a unique location (LOC) of each energy asset is denoted by
a pair of unique (NAE and DX). In this case, the result is five
pairs of NAE, DX identifiers representing the location of five
different and unique energy assets in the BMS network. Specifically
a search is made for all data points that contain the strings "NAE"
followed by at least one digit AND "DX" followed by at least one
digit to obtain the unique locations for the example PL:
TABLE-US-00001 NAE1 -> DX-24 NAE14 -> DX-1 NAE14 -> DX-80
NAE15 -> DX-20 NAE15 -> DX-25.
Next, all data labels of a unique location are retrieved. See FIG.
6 which illustrates schematically step 108 being performed in this
exemplary scenario to extract the data labels of the energy assets
in the BMS network. In this particular example, the following
labels were extracted for the unique locations shown immediately
above (and in FIG. 5):
TABLE-US-00002 NAE1 -> DX-24 -> CLG-VLV NAE1 -> DX-24
-> DA-H NAE1 -> DX-24 -> DA-T NAE1 -> DX-24 ->
DX-TRBL NAE1 -> DX-24 -> HTG-VLV NAE1 -> DX-24 ->
HUM-VLV NAE1 -> DX-24 -> MA-T NAE1 -> DX-24 -> OA-DPR
NAE1 -> DX-24 -> PH-T NAE1 -> DX-24 -> SF-O NAE1 ->
DX-24 -> SF-S NAE1 -> DX-24 -> ZN-T NAE14 -> DX-1 ->
CLG-VLV NAE14 -> DX-1 -> HTG-VLV NAE14 -> DX-1 ->
HUM-VLV NAE14 -> DX-1 -> PH-T NAE14 -> DX-1 -> SF-O
NAE14 -> DX-1 -> SF-S NAE14 -> DX-1 -> ZN-H NAE14 ->
DX-80 -> CLG-VLV NAE14 -> DX-80 -> DA-H NAE14 -> DX-80
-> DA-T NAE14 -> DX-80 -> HTG-VLV NAE14 -> DX-80 ->
HUM-VLV NAE14 -> DX-80 -> PH-T NAE14 -> DX-80 -> SF-O
NAE14 -> DX-80 -> ZN-H NAE15 -> DX-20 -> CLG-VLV NAE15
-> DX-20 -> DA-H NAE15 -> DX-20 -> DA-T NAE15 ->
DX-20 -> HTG-VLV NAE15 -> DX-20 -> HUM-JCKT NAE15 ->
DX-20 -> HUM-VLV NAE15 -> DX-20 -> OA-DPR NAE15 ->
DX-20 -> SA-P NAE15 -> DX-20 -> SF-O NAE15 -> DX-20
-> SF-S NAE15 -> DX-20 -> ZN-H NAE15 -> DX-20 ->
ZN-T
Given the labels of the unique assets, the asset type is then
determined. See FIG. 7 which illustrates schematically step 110
being performed in this exemplary scenario to determine the types
of energy assets from the extracted data labels. By way of example
only, as shown in FIG. 6, if data points labeled "CLG-VLV" and
"HTG-VLV" are present but none of the labels "HD-T," "CD-T,"
"MA-T," and "COIL-T" then the asset type of that location is a
Single Duct Variable Temp 100% Fresh air (SDVATF) AHU (see rule 3).
The asset types for the exemplary unique locations are shown
as:
TABLE-US-00003 NAE1 -> DX-24 -> SDVATR NAE14 -> DX-1 ->
SDVATF NAE14 -> DX-80 -> SDVATF NAE15 -> DX-20 ->
SDVATF
Finally, the BEMS rules are configured that are applicable for the
particular energy asset types. See FIG. 8 which illustrates
schematically step 112 being performed in this exemplary scenario
to configure BEMS rules for the asset types. For instance in this
exemplary scenario, for Air Handling Units (AHUs) of type SDVATF
the rule for simultaneous heating and cooling is applicable:
TABLE-US-00004 IF Heating Valve Percentage (HTG-VLV) >= 2% AND
Cooling Valve Percentage (CLG-VLV) >= 2% THEN trigger ALARM
In the instant example, the following instantiations are
obtained:
TABLE-US-00005 IF Metasys>RSTNAE14>N2-1>DX-1>HTG-VLV
>= 2% AND Metasys>RSTNAE14>N2-1>DX-1>CLG-VLV >=
2% THEN trigger ALARM IF
Metasys>RSTNAE14>N2-1>DX-80>HTG-VLV >= 2% AND
Metasys>RSTNAE14>N2-1>DX-80>CLG-VLV >= 2% THEN
trigger ALARM IF Metasys>RSTNAE15>N2-1>DX-20>HTG-VLV
>= 2% AND Metasys>RSTNAE15>N2-1>DX-20>CLG-VLV >=
2% THEN trigger ALARM
Turning now to FIG. 9, a block diagram is shown of an apparatus 900
for implementing one or more of the methodologies presented herein.
By way of example only, apparatus 900 can be configured to
implement one or more of the steps of the methodology of FIG. 1 for
configuring a BEMS of a site.
Apparatus 900 includes a computer system 910 and removable media
950. Computer system 910 includes a processor device 920, a network
interface 925, a memory 930, a media interface 935 and an optional
display 940. Network interface 925 allows computer system 910 to
connect to a network, while media interface 935 allows computer
system 910 to interact with media, such as a hard drive or
removable media 950.
As is known in the art, the methods and apparatus discussed herein
may be distributed as an article of manufacture that itself
comprises a machine-readable medium containing one or more programs
which when executed implement embodiments of the present invention.
For instance, when apparatus 900 is configured to implement one or
more of the steps of the methodology of FIG. 1 the machine-readable
medium may contain a program configured to derive a knowledge base
from subject matter expertise; use the knowledge base to extract
one or more building characteristics from a point list of a
building management system (BMS) that are not directly available
from the BMS point list; and configure the BEMS using the one or
more building characteristics extracted from the BMS point
list.
The machine-readable medium may be a recordable medium (e.g.,
floppy disks, hard drive, optical disks such as removable media
950, or memory cards) or may be a transmission medium (e.g., a
network comprising fiber-optics, the world-wide web, cables, or a
wireless channel using time-division multiple access, code-division
multiple access, or other radio-frequency channel). Any medium
known or developed that can store information suitable for use with
a computer system may be used.
Processor device 920 can be configured to implement the methods,
steps, and functions disclosed herein. The memory 930 could be
distributed or local and the processor device 920 could be
distributed or singular. The memory 930 could be implemented as an
electrical, magnetic or optical memory, or any combination of these
or other types of storage devices. Moreover, the term "memory"
should be construed broadly enough to encompass any information
able to be read from, or written to, an address in the addressable
space accessed by processor device 920. With this definition,
information on a network, accessible through network interface 925,
is still within memory 930 because the processor device 920 can
retrieve the information from the network. It should be noted that
each distributed processor that makes up processor device 920
generally contains its own addressable memory space. It should also
be noted that some or all of computer system 910 can be
incorporated into an application-specific or general-use integrated
circuit.
Optional display 940 is any type of display suitable for
interacting with a human user of apparatus 900. Generally, display
940 is a computer monitor or other similar display.
Although illustrative embodiments of the present invention have
been described herein, it is to be understood that the invention is
not limited to those precise embodiments, and that various other
changes and modifications may be made by one skilled in the art
without departing from the scope of the invention.
* * * * *